Method and apparatus for searching biometric image data

ABSTRACT

A method for matching biometric data is disclosed. A biometric information source is sensed to provide an image thereof. The image is then analysed to extract features thereform. A feature is selected as a first feature and a plurality of polygons are generated with a location of the first feature as a vertex of each. The polygons are then used to search a lookup table in order to determine an orientation and translation of the image relative to stored reference data.

FIELD OF THE INVENTION

[0001] The invention relates generally to searching of biometric imagedata and more particularly to a method of searching through biometricimage templates for a match to sensed biometric image data.

BACKGROUND OF THE INVENTION

[0002] Biometric authentication systems are now commonplace. Mostbiometric imaging systems relate a sensed biometric image and a knownbiometric template, one to the other. Such a system is referred to as aone-to-one authentication system. Using such a system, a sensed image isgenerally analysed within a frame of reference common to the frame ofreference in which the template was extracted. From the analysed sensedbiometric data, feature data is extracted within the known frame ofreference. This extracted feature data is then registered against thebiometric template.

[0003] Another form of biometric authentication system relies on aone-to-many comparison process. In the one to many authenticationprocess, a sensed image is generally analysed within a frame ofreference common to the frame of reference in which all of the templateswere extracted. From the analysed sensed biometric data, feature data isextracted within the known frame of reference. This extracted featuredata is then registered against each of the biometric templates. Themost close match is then selected as the “authentication.”Unfortunately, in such a system, the registration process is either morecomputationally expensive resulting in better authentication or of aless computationally expensive nature providing for faster execution.

[0004] One method to speed up the process of registration in aone-to-many biometric authentication system involves dividing thebiometric templates based on characteristics and then, by identifyingthe characteristics, only registering the feature data against thebiometric templates having similar characteristics. For example,fingerprints are groupable based on the fingerprint type—loop, swirl,etc. Thus, registration of the feature data is only performed againstsome of the template. Unfortunately, some feature data is difficult toclassify resulting in less of an advantage to the above method thanmight be expected. Further, it is difficult to group fingerprints intosmall enough groupings due to the general nature of fingerprintclassification and difficulties in accurately classifying fingerprints.

[0005] Also, the use of a subset of, for example, a fingerprint image asa PIN is difficult. Fingerprints and other biometric information sourcesare not truly repeatable in nature. A fingertip may be drier or wetter.It may be more elastic or less. It may be scratched or dirty or clean.Each of the above listed conditions affects the fingerprint image and,as such, means that the image subset may very well differ. Typical PINanalysis requires provision of the unique and static PEN. Here, such amethod will result in a system that is very inconvenient to use.

[0006] It is an object to provide a method of identifying an individualthat overcomes the limitations of the prior art.

SUMMARY OF THE INVENTION

[0007] In accordance with an aspect of the invention there is provided amethod for use in biometric authentication comprising the steps of: a)sensing a biometric information source to provide biometric data; b)determining a plurality of feature locations, the feature locations offeatures within the biometric data; c) for a first feature location ofthe plurality of feature locations forming a plurality of at least aline each based on some of the plurality of feature locations includingthe first feature location; and, d) forming at least a lookup tableentry based on characteristics of the plurality of at least a line.

[0008] In accordance with the invention there is also provided a methodfor use in biometric authentication comprising the steps of: a) sensinga biometric information source to provide biometric data; b) determininga plurality of feature locations, the feature locations of featureswithin the biometric data; c) for a first feature location of theplurality of feature locations forming a plurality of at least a lineeach based on some of the plurality of feature locations including thefirst feature location; and, d) searching within a lookup table forentries substantially similar to the plurality of at least a line todetermine alignment data relating to the biometric data.

[0009] In accordance with the invention there is also provided a methodfor use in biometric authentication comprising the steps of: a) sensinga biometric information source to provide biometric data; b) determininga plurality of feature locations, the feature locations of featureswithin the biometric data; c) for a first feature location of theplurality of feature locations forming a plurality of at least a lineeach based on some of the plurality of feature locations including thefirst feature location; and, d) searching within a lookup table forentries substantially similar to the plurality of at least a line tofilter a plurality of templates to identify some templates with whichalignment of the biometric data is likely.

[0010] In accordance with the invention there is also provided a methodfor use in biometric authentication comprising the steps of: sensing abiometric information source to provide biometric data; determining aplurality of feature locations, the feature locations of features withinthe biometric data; for a first feature location of the plurality offeature locations forming a plurality of at least a line each based onsome of the plurality of feature locations including the first featurelocation; and, forming at least a lookup table entry based oncharacteristics of the plurality of at least a line; sensing anotherbiometric information source to provide further biometric data;determining a further plurality of feature locations, the featurelocations of features within the further biometric data; for a furtherfirst feature location of the further plurality of feature locationsforming a further plurality of at least a line each based on some of thefurther plurality of feature locations including the further firstfeature location; and, searching within a lookup table for entriessubstantially similar to the further plurality of at least a line.

[0011] In accordance with another aspect of the invention there isprovided an authentication database comprising: a lookup table includingat least a lookup table entry based on characteristics of a plurality oflines for a first feature location of a plurality of feature locationsextracted from a biometric information sample each line based on some ofthe plurality of feature locations including the first feature location.

[0012] In accordance with yet another aspect of the invention there isprovided a memory having data stored thereon, the data relating to aplurality of executable instructions for, when executed resulting inperformance of the steps of: determining a plurality of featurelocations, the feature locations of features within the biometric data;and, for a first feature location of the plurality of feature locationsforming a plurality of at least a line each based on some of theplurality of feature locations including the first feature location.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The invention will now be described with reference to theattached drawings in which:

[0014]FIG. 1a is a simplified diagram of a portion of a fingerprintridge valley pattern;

[0015]FIG. 1b is a simplified diagram of the portion of a fingerprintridge valley pattern with features thereof indicated;

[0016]FIG. 2 is a simplified flow diagram of a method according to theinvention;

[0017]FIG. 3 is a simplified diagram of the portion of a fingerprintridge valley pattern with features thereof indicated and a first featureindicated;

[0018]FIG. 4 is a simplified diagram of the portion of a fingerprintridge valley pattern with features thereof indicated and a first featureindicated and a plurality of polygons in the form of triangles formedwith a vertex at the first feature;

[0019]FIG. 5 is a simplified diagram of the portion of a fingerprintridge valley pattern with features thereof indicated and a first featureindicated and a plurality of polygons in the form of triangles formedwith a vertex at the first feature and a longest side of each polygonhighlighted;

[0020]FIG. 6 is a simplified diagram of the portion of a fingerprintridge valley pattern with features thereof indicated and a first featureindicated and a plurality of polygons in the form of triangles formedwith a vertex at the first feature and an angle opposite a longest sideof each polygon highlighted;

[0021]FIG. 7a is a simplified flow diagram of a method of performing aone to one match;

[0022]FIG. 7b is a simplified flow diagram of a method of performing aone to one match;

[0023]FIG. 8 is a simplified flow diagram of a method of performing aone to many match; and,

[0024]FIG. 9 is a simplified flow diagram of a method of performing aone to many match from a plurality of templates of a same biometricinformation source is shown.

DETAILED DESCRIPTION OF THE INVENTION

[0025] The following description is presented to enable a person skilledin the art to make and use the invention, and is provided in the contextof a particular application and its requirements. Various modificationsto the disclosed embodiments will be readily apparent to those skilledin the art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andthe scope of the invention. Thus, the present invention is not intendedto be limited to the embodiments disclosed, but is to be accorded thewidest scope consistent with the principles and features disclosedherein. In particular, the invention is described with reference tofingerprints but it is to be completely understood that the inventionalso works with other forms of biometric information.

[0026] Referring to FIG. 1a, a pre-processed simplified portion of afingerprint about its core is shown. The image is filtered using imageprocessing filtering techniques and the contrast is adjusted to anormalized level. The fingerprint comprises ridges 1 and valleys 2.Alternatively, the image is inverted resulting in ridges 1 beingrepresented in white instead of black. Within the image, features 6 arepresent as highlighted in FIG. 1b. The features include ridge endingsand bifurcations. Of course, other feature types are also known and, inimplementing the present invention feature selection from any knownfeatures is possible. Preferably, features are selected havingaccurately identifiable locations.

[0027] Referring to FIG. 2, a simplified flow diagram of a methodaccording to the invention is shown. A fingerprint image is captured instep 10. The image is typically of a larger area of a fingertip thanthat shown in FIG. 1a, but the image of FIG. 1a is provided forexplanation of the present embodiment for clarity. The fingerprint imageis preprocessed in step 11 resulting in a preprocessed image, asimplified version of which is shown in FIG. 1a. The fingerprint imageis then analyzed in step 12 to determine a plurality of features andtheir locations shown in FIG. 1b. From the plurality of features, afirst feature, shown as 35 in FIG. 3, is selected at step 13. Thefeature location of the first feature is then used in the formation of aplurality of polygons. Hereinbelow, the method is described using apreferred polygon, the triangle, though other polygons are alsosupported.

[0028] In step 14, and shown in FIG. 4, a plurality of triangles areformed having vertices at each of three feature locations one of whichis the location of the first feature 35. Though only four triangles areshown for clarity, one of skill in the art will appreciate that manymore triangles are typically formed. For example, when all triangles areformed using the six (6) closest features, this results in 6!2 (!Indicating the choose function) triangles or a total of 15 triangles allhaving the first feature location as a vertex thereof.

[0029] In step 15, a side of each triangle is selected as the referenceside. Here, the longest side of each triangle is selected as the firstside as shown in FIG. 5. In step 16, each triangle is normalized withtheir longest side first and sides presented in a clockwise order, thenormalization of polygon representation. This maintains polygon scalewhile rendering each polygon related data record a unique representationrepeatably determinable from a same polygon. When using such a method,scaling of the sensed biometric data is performed prior to polygonextraction.

[0030] Alternatively, scale normalization is performed where the side oflongest length is represented with a reference value for length thereof.For example, each longest side is set to a maximum length, based on anumber of bits used to store a side length. Then the shorter sides arestored as proportions of the longest side. Of course, since all longestsides are stored as a same side length, there is no need to store alength of the longest side. Alternatively, all triangles are normalizedrelative to a first triangle so relative size of different triangles ismaintained. Normalisation is a useful tool as it prevents effects suchas scaling caused by use of different biometric sensing apparatus fromsignificantly affecting the present method from functioning adequately.

[0031] In step 17, a vertex of each triangle is selected as a firstvertex as shown in FIG. 6. In an embodiment, the first vertex is thevertex opposite the first side. By ordering of sides and vertices in aknown ordering, the resulting data is in a consistent form forcomparison.

[0032] Once the triangles are stored as a record, this record forms anindexable and searchable item within a database. Thus, a single recordis useful for a one to one biometric image match to form a “quick anddirty” estimation of registrability. That said, when the record isstored in a database with many records, it is possible to search thedatabase in order to reduce a number of registration attempts requiredto identify an individual using a one-to-many biometric authenticationprocess.

[0033] Referring to FIG. 7a, a method of performing a one-to-one matchis shown for use in match-on-card applications. Here, as littlebiometric data as is possible is provided from a secure storage deviceto a second other processor for use in alignment of sensed biometricdata within a known frame of reference. Aligning features of a biometricimage from one sample to another sample is a difficult problem evengiven all the template data. Often the two samples have very littleimage area of intersection, the images are sometimes rotated relative toeach other, sometimes image distortion exists and some features that arepresent in one image are not present in another image. Determining anoptimal alignment in a reasonable amount of processing time is key tobeing able to reliably match a fingerprint. Of course the optimalalignment must be determined and then produced in the reasonable time soit is important that the determination be made quickly to provide timefor translation and rotation of the fingerprint image.

[0034] In a Match-On-Card environment, the problem is more complexbecause the number of features used for alignment is preferably kept toa minimum. For example, only the minutia locations are provided and,optionally, these are transformed for enhanced security. Thetransformation of the minutia locations introduces further error betweensamples and makes the problems associated with alignment moresignificant. Some images have so many false minutia points that anattempt to manually perform an alignment is unlikely to succeed.

[0035] According to the invention, biometric data is captures at step70. features are extracted therefrom, step 71, each having a featurelocaiton. At step 72, a feature is selected as the first feature. Aplurality of polygons is formed, step 73, based on the featurelocations. The polygons are used to search for matching polygons, step74, in order to align the images. The method is useful with any featureand with numerous polygons; however, for the purposes of explanation,fingerprint minutia locations are used with triangular polygons.

[0036] In processing of a reference fingerprint, for each featurelocation (X,Y) the nearest N features are found. All combinations of 2features are chosen from the N features and N!2 triangles are formedusing the sets of 2 features. The triangles are stored in a normalizedform and stored in a O(1) lookup table. Features about the triangle thatare useful are, for example, the length of each side, the type offeature present at each vertex, the orientation of each feature at eachvertex, and the value of each feature at each vertex. Of course, anglesbetween sides are also useful though a triangle is clearly defined bylengths of three sides and their relative ordering—lengths alonedefining two triangles exhibiting mirror symmetry. Alternatively, anyfeature data desired for storage within the triangle data is possible.When authentication is implemented on more than one processor in adistributed fashion, security concerns may limit available features foruse in alignment.

[0037] A person of skill in the art will quickly understand that theabove method generates multiple copies of a same triangle when more thanone feature location (Xi,Yi) are used. This might result in unreasonableweighting of some features relative to others in an alignment processsince more than one match will occur when the duplicate triangle ispresent in the live fingerprint sample. Therefore, in an embodimenttriangles are stored only once and the O(1) lookup table allows for aquick lookup to determine if a triangle has already been stored andprevent multiple entries of the same triangle. Alternatively, in anotherembodiment triangles are stored several times, but during alignment eachtriangle is only compared to one matching entry using a filter in thealignment process instead of in the lookup table construction process.

[0038] Further alternatively, a graph is maintained for eachtranslation-rotation pairing. As each polygon is matched, its rotationand translation is determined and a graph associated with the rotationand the translation is updated.

[0039] Once the lookup table is produced, a plurality of triangles isknown and their orientation and position in a known frame of referenceis also known. Thus, determining an orientation and position of similartriangles in a live fingerprint sample, allows for alignment of the livefingerprint sample within the known frame of reference.

[0040] Using a live fingerprint sample, a similar process is performedto extract triangles; however, each triangle generated is matchedagainst all similar triangles within the lookup table. The O(1) lookuptable facilitates this lookup without resulting in a significantperformance degradation. Each triangle matched is stored in a bipartitegraph as a possible matching of features. The weight of the match in thebipartite graph indicates a similarity between the two triangles.

[0041] Once a reasonable match is formed, alignment is known and theresulting live fingerprint data is comparable to the templatefingerprint data in the known frame of reference. For example, the livefingerprint data is translated and rotated such that the triangles thatmatch overlay one on another. Here, the live fingerprint data isextracted within the known frame of reference and provided to a secureprocessor for comparison against secure template data. Of course, sincethe triangles for single first feature 35 are all connected at thatfeature, the alignment is performable with reasonable accuracy.

[0042] Alternatively, as shown in FIG. 7b, multiple bipartite graphs areused to determine a rotation of the live fingerprint data. A template isprovided for each discrete rotation of the reference data, step 700. Abiometric information sample is sensed to provide biometric data, 701.The biometric data is analysed to extract features therefrom, step 702,each feature having a feature location. A plurality of polygons isdefined based on the feature locations, step 703. The polygons arecompared to reference polygons in a lookup table, step 704, and aplurality of graphs are maintained—one relating to each template—step705. The triangles are matched in an other than rotationally independentfashion. The following property of matching triangles is noteworthy:Matching triangles will all have a similar rotation, but non-matchingsimilar triangles will have rotations that are distributed among eachpossible rotation at random. Using a separate bipartite graph for eachrotation removes a lot of noise from the bipartite graph thatcorresponds to the correct rotation.

[0043] Further alternatively, multiple bipartite graphs are used todetermine a translation of the live fingerprint data. A graph isprovided for each discrete translation of the live data and a graph ismaintained relating to each template. The triangles are matched in another than translationally independent fashion. =Using a separatebipartite graph for each translation removes a lot of noise from thebipartite graph that corresponds to the correct translation.

[0044] The bipartite graph with the strongest weightings is chosen. Atthis point the correct rotation is known. To determine the rest of thetransformation required to align the reference sample with the livesample, the translation is also needed. The mode of the translations ischosen for both the X and Y coordinates. The image is then translatedand compared against the template associated with its rotation.Alternatively, a rotation amount is determined and the image istransformed in orientation according to the determined rotation.

[0045] Alternatively translation is determined using the trianglematching method first and rotation is determined based on the results ofthe first step. Further alternatively, a template is stored for each ofa plurality of rotations and the alignment is either performed byselecting a template with a matching rotation or by rotating to a mostappropriate template. For example, if templates are provided at 1 degreerotations for 180 degrees, the live fingerprint image is either providedas is or is rotated 180 degrees—a relatively computationally simpletransformation.

[0046] Referring to FIG. 8, a method of performing a one-to-many matchis shown. Here, as little biometric data as is possible is provided froma secure storage device in the form of a secure server to second otherprocessor for use in alignment of sensed biometric data within a knownframe of reference. Aligning features of a biometric image from onesample to another sample remains a difficult problem. Further, a goodalignment method will also act as a filter eliminating many potentialidentifications prior to template registration. Of course, when theentire process is performed on a same secure server, then all thetemplate data is usable in performing alignment allowing for moreaccurate filtering in most cases.

[0047] In a one-to-many environment, template registration on fewertemplates is advantageous as it improves overall system security andreliability. When an authentication server is used, it is advantageousto maintain a number of features used for alignment at a minimum numberto reduce network traffic. Of course alignment data is provided from theauthentication server for every fingerprint within the templatedatabase. Alternatively, the alignment data is public and is stored oneach workstation on the network. According to the invention, the problemis once again solved using a polygon method. The method is useful withany feature; however, for the purposes of explanation, fingerprintminutia locations are used with triangular polygons.

[0048] A biometric information source in the form of a fingertip issensed to provide a fingerprint at step 80. The fingerprint is analysedto extract features having feature locations therefrom at step 81. Inprocessing of a reference fingerprint to form a template, for a numberof feature locations (X,Y) the nearest N features are found, step 82.All combinations of 2 features are chosen from the N features and N!2triangles are formed using the sets of 2 features, step 83. It has beenfound that a value of N around 12 is useful as the number of trianglesis not so large and yet a reasonable number of triangles is formed. N ispreferably chosed to overcome problems of false features present in thereference fingerprint sample. The triangles are stored in a normalizedform and stored in a O(1) lookup table, step 84. Features about thetriangle that are useful are, for example, the length of each side, thetype of feature present at each vertex, the orientation of each featureat each vertex, and the value of each feature at each vertex. Of course,angles between sides are also useful though a triangle is clearlydefined by lengths of three sides and their relative ordering—lengthsalone defining two triangles exhibiting mirror symmetry.

[0049] A person of skill in the art will quickly understand that theabove method generates multiple copies of a same triangle when more thanone feature location (Xi,Yi) are used. This might result in unreasonableweighting of some features relative to others in an alignment process.Therefore, triangles are stored only once and the O(1) lookup tableallows for a quick lookup to determine if a triangle has already beenstored and prevent multiple entries of the same triangle, step 85.Alternatively, triangles are stored several times, but during alignmenteach triangle is only compared to one matching entry using a filter inthe alignment process instead of in the lookup table constructionprocess.

[0050] Preferably, the live fingerprint sample is sensed, step 86, andprocessed in a similar fashion, step 87, forming a listing of normalizedpolygons in the form of triangles that is filtered to remove duplicates,step 88. This allows for each polygon to maintain a same weighting inappropriate graphs as it is only represented once in each list.

[0051] Once the lookup table is produced, a plurality of triangles isknown and their orientation and position in a known frame of referenceis also known. Thus, a step of determining an orientation and positionof similar triangles determined from the live fingerprint sample isperformed, step 89, allowing for alignment of the live fingerprintsample within the known frame of reference. Failure to determine anorientation and position of similar triangles in the live fingerprint isindicative of a poor match and template registration for thatfingerprint template is obviated.

[0052] Using a live fingerprint sample, a similar process is performedto extract triangles; however, each triangle generated is matchedagainst all similar triangles within the lookup table. The O(1) lookuptable facilitates this lookup without resulting in a significantperformance degradation. Each triangle matched is stored in a bipartitegraph as a possible matching of features. The weight of the match in thebipartite graph indicates a similarity between the two triangles. Thereis a bipartite graph for each template such that only aligned templatesare then registered against the live fingerprint data to determine alikelihood of a match. Because the image alignment is performable withstatistically valid reliability, those triangles relating to templatesand for which no match is found—alignment with whose template does notoccur—act as filters to limit a number of template registrationsrequired.

[0053] Once a list of reasonable matches is formed, alignment is knownfor comparisons with each of the templates within the list and theresulting live fingerprint data is comparable to each of the templatefingerprint data within each said known frame of reference. For example,the live fingerprint data is translated and rotated such that thetriangles that match overlay one on another one template at a time.Here, the live fingerprint data is extracted within the known frame ofreference and provided to a secure processor for comparison against theassociated template data, step 890.

[0054] The lookup table functions adequately with multiple fingerprintswithout loss of speed. In effect, an O(1) lookup can be performedagainst 100 fingerprints instead of just one print. This means that thisidea will work with a limited one-to-many. However, as more prints areadded to the lookup table, more memory is needed and the amount of noisewithin the table also increases. Distributed processing of the lookupoperations on numerous processors is useful in alleviating the memoryrequirements for some implementations.

[0055] Referring to FIG. 9, a method of selecting templates from aplurality of templates of a same biometric information source is shown.This method is identical to the one to many application set out withreference to FIG. 8, but here the “many” templates all relate tobiometrics of a same individual. For example, to authenticate someoneusing any one of their ten fingerprints requires that a systemdistinguish adequately between each of the individual's fingerprints.The present method facilitates discrimination between fingerprints forsuch an application.

[0056] As will be evident to those of skill in the art, normalizationand sorting or indexing of lookup table entries allows for fastefficient searching of the lookup table.

[0057] The present inventive method is implementable in a distributedprocessing environment since each of several processors may be provideddifferent lookup tables and feature locations from the live fingerprintdata to determine potential matches. For very large sets of features andlarge one-to-many systems, this is advantageous as distributedprocessing will improve overall system performance in those cases.

[0058] Often, it is desired to compare the live sample against multiplereference samples. A user may have multiple fingers enrolled, or theremay be multiple users that need to be checked. It is preferable to usean O(1) lookup such that aligning against 100 reference samples doesn'ttake any longer than aligning against one sample.

[0059] In accordance with an experimental implementation, the lookuptable was O(1) and was indexed first by the length of the perimeter andsecondly by the length of each side. Alternatively, the lookup table isindexed by feature type and then by a ratio of lengths of sides. Furtheralternatively, the lookup table is indexed based on the angles betweeneach pair of sides of each polygon. In another embodiment, indexing isperformed based on minutia directions relative to side angles. In yetanother embodiment, indexing is performed based on minutia directionsrelative to each other in a predetermined ordering.

[0060] Preferably, only features within a certain neighborhood of thefirst feature are relied upon or those features within the certainneighbourhood are weighted more heavily in the graphs. This reduces oreliminates error due to skewing and stretching of the biometric sampleand also allows an alignment to occur even when the overlap region issmall. Of course, longer lines are typically more prone to error but theerrors are likely small relative to the line length. Further, longerlines result in better rotational values for a rotational offset as theyare less susceptible to quantization error resulting from a resolutionof the sensing apparatus.

[0061] Preferably, for each feature, multiple other features of thefingerprint are grouped therewith forming polygons. This reduces oreliminates alignment problems due to missing and/or false features.

[0062] Many situations where limited one-to-many matching systems are inuse including free-seating systems where multiple people are sharingmultiple computers. It is possible to distribute each matching attemptacross all the systems and use the spare computation power of some orall of the systems to align a sensed live fingerprint against eachreference template. In effect, this allows a limited one-to-many andsignificantly reduces the noise that is introduced by attempting toalign a live sample against a large number of multiple referencetemplates.

[0063] Though the invention is described with reference to triangularpolygons, it is understood by one of skill in the art that a polygon isdefinable without fully specifying all parameters thereof. For example,a triangle is defined by a two side lengths and an angle or by a sidelength and the two angles at each end thereof and their orientationrelative to the line or by three side lengths and an order thereof.

[0064] In another embodiment, a plurality of feature locations is usedto uniquely define shapes other than polygons. For example, threefeature locations are used to define an ellipse having values of a and brelated to distances from the first feature location to each of theother two features and having a position thereof related to one or moreof the feature locations. Thus, as long as the shape definition isapproximately unique to the placement of the feature locations,alignment data is determinable from the shapes as is searchability. Forthe ellipse presented in the previous example, indexing is performableon a and b, for example.

[0065] In yet another embodiment, instead of shapes being defined basedon feature locations, lines are defined. This provides for a very simpleprocess defining a line between every feature and the first feature.Since a line is only dependent on two points, it is not necessary tolimit the area over which lines are formed. Errors in line alignment andabsent features, become identifiable in a more straightforward mannersince there is not an issue of which of the vertices caused the polygonto not be found. Further, lines provide excellent rotational andtranslational information. Effectively, for a single first point, agraph so formed is representative of a pattern emanating from the firstpoint to each feature within an image—a series of lines emanating from asame point. Though this is not a plurality of polygons or shapes, theresulting pattern is searchable as a plurality of line elements reducingthe effects of absent features or distortion within one or both images.

[0066] Of course, using lines also enhances processing time since thelines do not require normalization, are easily sorted by length, and areeasily identifiable. Using lines is highly advantageous in manyapplications and more specifically when other feature data such asfeature type and direction is known. Thus a line becomes more than justa distance between points. It is a distance between points having knownand easily identifiable qualities. As such, accurate correlation betweenlines is facilitated.

[0067] Numerous other embodiments may be envisaged without departingfrom the spirit and scope of the invention.

What is claimed is:
 1. A method for use in biometric authenticationcomprising the steps of: a) sensing a biometric information source toprovide biometric data; b) determining a plurality of feature locations,the feature locations of features within the biometric data; c) for afirst feature location of the plurality of feature locations forming aplurality of at least a line each based on some of the plurality offeature locations including the first feature location; and, d) formingat least a lookup table entry based on characteristics of the pluralityof at least a line.
 2. A method according to claim 1, comprising thestep of filtering the plurality of at least a line to remove duplicateat least a line therefrom.
 3. A method according to claim 1, wherein thelines have endpoints each associated with a feature of the plurality offeatures, one of the endpoints associated with the first feature.
 4. Amethod according to claim 3, wherein the lines have endpoints each at alocation corresponding to a feature of the plurality of features, one ofthe endpoints at a location corresponding to the first feature location.5. A method according to claim 3, wherein the lines have endpoints eachat a location corresponding to transformed feature location of a featureof the plurality of features, one of the endpoints at a locationcorresponding to a transformed location of the first feature, thetransform in the form of a known repeatable function.
 6. A methodaccording to claim 1, wherein each of the at least a line includes alength.
 7. A method according to claim 1, wherein the plurality of atleast a line is a curve.
 8. A method according to claim 7, wherein thecurve defines a closed curved shape.
 9. A method according to claim 1,wherein the lookup table is ordered.
 10. A method according to claim 1,wherein the lookup table is indexed.
 11. A method according to claim 1,wherein the plurality of at least a line each defines a polygon.
 12. Amethod according to claim 11, wherein the polygons are triangles.
 13. Amethod according to claim 11, wherein the polygons have vertices eachassociated with a feature of the plurality of features, one of thevertices associated with the first feature.
 14. A method according toclaim 13, wherein the polygons have vertices each at a locationcorresponding to a feature of the plurality of features, one of thevertices at a location corresponding to the first feature location. 15.A method according to claim 13, wherein the polygons have vertices eachat a location corresponding to transformed feature location of a featureof the plurality of features, one of the vertices at a locationcorresponding to a transformed location of the first feature, thetransform in the form of a known repeatable function.
 16. A methodaccording to claim 11, wherein the polygons are stored in a normalizedand reproducible form.
 17. A method according to claim 16, wherein thelookup table is ordered in dependence upon the reproducible form.
 18. Amethod according to claim 16, wherein the lookup table is indexed independence upon the reproducible form.
 19. A method according to claim11, comprising the step of: iterating the step (c) for each of pluralityof different first feature locations.
 20. A method according to claim11, comprising the step of: iterating the steps a-c for each ofplurality of different biometric information sources, the lookup tableincluding entries relating to a plurality of different biometricinformation sources.
 21. A method according to claim 20, wherein thelookup table includes entries relating to a plurality of differentbiometric information sources of a same individual.
 22. A methodaccording to claim 20, wherein the lookup table includes entriesrelating to a plurality of different biometric information sources ofdifferent individuals.
 23. A method for use in biometric authenticationcomprising the steps of: a) sensing a biometric information source toprovide biometric data; b) determining a plurality of feature locations,the feature locations of features within the biometric data; c) for afirst feature location of the plurality of feature locations forming aplurality of at least a line each based on some of the plurality offeature locations including the first feature location; and, d)searching within a lookup table for entries substantially similar to theplurality of at least a line.
 24. A method according to claim 23,comprising the step of: e) determining alignment data relating to thebiometric data and the further biometric data.
 25. A method according toclaim 23, comprising the step of: based on the results of step (d)identifying some entries within the lookup table with which alignment ofthe further biometric data is likely.
 26. A method according to claim23, comprising the step of filtering the plurality of at least a line toremove duplicate at least a line therefrom.
 27. A method according toclaim 23, wherein the lines have endpoints each associated with afeature of the plurality of features, one of the endpoints associatedwith the first feature.
 28. A method according to claim 27, wherein thelines have endpoints each at a location corresponding to a feature ofthe plurality of features, one of the endpoints at a locationcorresponding to the first feature location.
 29. A method according toclaim 27, wherein the lines have endpoints each at a locationcorresponding to transformed feature location of a feature of theplurality of features, one of the endpoints at a location correspondingto a transformed location of the first feature, the transform in theform of a known repeatable function.
 30. A method according to claim 23,wherein the plurality of at least a line includes a length.
 31. A methodaccording to claim 23, wherein the plurality of at least a line definesa curve.
 32. A method according to claim 31, wherein the curve defines aclosed curved shape.
 33. A method according to claim 23, wherein theplurality of at least a line each defines a polygon.
 34. A methodaccording to claim 33, wherein the polygons are triangles.
 35. A methodaccording to claim 33, wherein the polygons have vertices eachassociated with a feature of the plurality of features, one of thevertices associated with the first feature.
 36. A method according toclaim 35, wherein the polygons have vertices each at a locationcorresponding to a feature of the plurality of features, one of thevertices at a location corresponding to the first feature location. 37.A method according to claim 35, wherein the polygons have vertices eachat a location corresponding to transformed feature location of a featureof the plurality of features, one of the vertices at a locationcorresponding to a transformed location of the first feature, thetransform in the form of a known repeatable function.
 38. A methodaccording to claim 33, comprising the step of: iterating the steps (c)and (d) for each of plurality of different first feature locations. 39.An authentication database comprising: a lookup table including at leasta lookup table entry based on characteristics of a plurality of linesfor a first feature location of a plurality of feature locationsextracted from a biometric information sample each line based on some ofthe plurality of feature locations including the first feature location.40. A method according to claim 39, wherein the lines have endpointseach associated with a feature of the plurality of features, one of theendpoints associated with the first feature.
 41. A method according toclaim 40, wherein the lines have endpoints each at a locationcorresponding to a feature of the plurality of features, one of theendpoints at a location corresponding to the first feature location. 42.A method according to claim 40, wherein the lines have endpoints each ata location corresponding to transformed feature location of a feature ofthe plurality of features, one of the endpoints at a locationcorresponding to a transformed location of the first feature, thetransform in the form of a known repeatable function.
 43. A methodaccording to claim 39, wherein the plurality of lines each defines acurve.
 44. A method according to claim 43, wherein the curve defines aclosed curved shape.
 45. A method according to claim 39, wherein thelookup table is ordered.
 46. A method according to claim 39, wherein thelookup table is indexed.
 47. A method according to claim 39, wherein theplurality of lines includes lines defining polygons.
 48. Anauthentication database according to claim 47, wherein the polygons aretriangles.
 49. An authentication database according to claim 47, whereinthe polygons have vertices each associated with a feature of theplurality of features, one of the vertices associated with the firstfeature.
 50. An authentication database according to claim 49, whereinthe polygons have vertices each at a location corresponding to a featureof the plurality of features, one of the vertices at a locationcorresponding to the first feature location.
 51. An authenticationdatabase according to claim 49, wherein the polygons have vertices eachat a location corresponding to transformed feature location of a featureof the plurality of features, one of the vertices at a locationcorresponding to a transformed location of the first feature, thetransform in the form of a known repeatable function.
 52. Anauthentication database according to claim 47, wherein the polygons arenormalized.
 53. An authentication database according to claim 39,comprising: a biometric sensor for sensing biometric information toprovide biometric data; a processor for a) receiving biometric data fromthe biometric sensor; b) determining a plurality of feature locations,the feature locations of features within the biometric data; c) for afirst feature location of the plurality of feature locations forming aplurality of at least a line each based on some of the plurality offeature locations including the first feature location; and, d)searching within a lookup table for entries substantially similar to theplurality of at least a line.
 54. A method for use in biometricauthentication comprising the steps of: a) sensing a biometricinformation source to provide biometric data; b) determining a pluralityof feature locations, the feature locations of features within thebiometric data; c) for a first feature location of the plurality offeature locations forming a plurality of at least a line each based onsome of the plurality of feature locations including the first featurelocation; d) forming at least a lookup table entry based oncharacteristics of the plurality of at least a line; e) sensing anotherbiometric information source to provide further biometric data; f)determining a further plurality of feature locations, the featurelocations of features within the further biometric data; g) for afurther first feature location of the further plurality of featurelocations forming a further plurality of at least a line each based onsome of the further plurality of feature locations including the furtherfirst feature location; and, h) searching within a lookup table forentries substantially similar to the further plurality of at least aline.
 55. A method according to claim 54, comprising the step of: i)determining alignment data relating to the biometric data and thefurther biometric data.
 56. A method according to claim 54, comprisingthe step of: i) based on the results of step (h) identifying someentries within the lookup table with which alignment of the furtherbiometric data is likely.
 57. A memory having data stored thereon, thedata relating to a plurality of executable instructions for, whenexecuted resulting in performance of the steps of: determining aplurality of feature locations, the feature locations of features withinthe biometric data; and, for a first feature location of the pluralityof feature locations forming a plurality of at least a line each basedon some of the plurality of feature locations including the firstfeature location.
 58. A memory as defined in claim 57, having datastored thereon, the data relating to a plurality of executableinstructions for, when executed resulting in performance of the step of:sensing a biometric information source to provide biometric data;
 59. Amemory as defined in claim 58, having data stored thereon, the datarelating to a plurality of executable instructions for, when executedresulting in performance of the step of: forming at least a lookup tableentry based on characteristics of the plurality of at least a line. 60.A memory as defined in claim 58, having data stored thereon, the datarelating to a plurality of executable instructions for, when executedresulting in performance of the step of: searching within a lookup tablefor entries substantially similar to the plurality of at least a line todetermine alignment data relating to the biometric data.
 61. A memory asdefined in claim 58, having data stored thereon, the data relating to aplurality of executable instructions for, when executed resulting inperformance of the step of: searching within a lookup table for entriessubstantially similar to the plurality of at least a line to filter aplurality of templates to identify some templates with which alignmentof the biometric data is likely.